Neural network surrogate models for equations of state

Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and use thermodynamic relationships to derive other necessary ther...

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Bibliographic Details
Published in:Physics of plasmas 2023-03, Vol.30 (3)
Main Authors: Mentzer, Katherine L., Peterson, J. Luc
Format: Article
Language:English
Subjects:
Energy value
Entropy
Equations of state
Errors
Inertial confinement fusion
Model accuracy
Neural networks
Plasma physics
Regression models
Thermodynamics
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Summary:Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and use thermodynamic relationships to derive other necessary thermodynamic EOS quantities. We incorporate phase information into the model by training a phase classifier and using phase-specific regression models, which improves the modal prediction accuracy. Our model predicts energy values to 1% relative error and entropy to 3.5% relative error in a log-transformed space. Although sound speed predictions require further improvement, the derived pressure values are accurate within 10% relative error. Our results suggest that neural network models can effectively model EOS for inertial confinement fusion simulation applications.
ISSN:1070-664X
1089-7674
DOI:10.1063/5.0126708